Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Ce cours fait partie de la Nombre de IBM AI Engineering Certificat Professionnel
Introduction to Deep Learning & Neural Networks with Keras
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À propos de ce cours
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Compétences que vous acquerrez
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
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Cours 3 sur 6 dans le
Dates limites flexibles
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Niveau intermédiaire
Approx. 9 heures pour terminer
Recommandé : 2 - 3 hours/week
Anglais
Sous-titres : Anglais
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.
2 heures pour terminer
4 vidéos (Total 17 min), 1 lecture, 2 quiz
4 vidéos
Welcome2 min
Introduction to Deep Learning4 min
Neurons and Neural Networks3 min
Artificial Neural Networks5 min
1 lecture
Syllabus10 min
1 exercice pour s'entraîner
Introduction to Neural Networks and Deep Learning30 min
1 heure pour terminer
Artificial Neural Networks
In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.
1 heure pour terminer
4 vidéos (Total 22 min)
1 exercice pour s'entraîner
Artificial Neural Networks30 min
3 heures pour terminer
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
3 heures pour terminer
3 vidéos (Total 16 min)
3 vidéos
Regression Models with Keras6 min
Classification Models with Keras5 min
1 exercice pour s'entraîner
Keras and Deep Learning Libraries30 min
2 heures pour terminer
Deep Learning Models
In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
2 heures pour terminer
4 vidéos (Total 17 min)
4 vidéos
Convolutional Neural Networks8 min
Recurrent Neural Networks2 min
Autoencoders2 min
1 exercice pour s'entraîner
Deep Learning Models30 min
Avis
4.7
Meilleurs avis pour INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
par ABMar 15, 2020
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
par FBMar 24, 2020
It is a really good course.\n\nThe labs could have had more for us to do, much of the labs was already implemented.\n\nStill, great introduction to the proposed subjects.
par MCFeb 19, 2020
Best suited for beginner in Deep learning with Keras. Good content, hands on experience with Jupiter notebook with IBM Developer tool. Worked Exercise code for CNN & RNN.
par ASMar 19, 2020
A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.
par RSFeb 20, 2020
I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course
par SUMar 10, 2020
try to add more case study problems and solve it on lectures so that we can understand how to start (initialize) the coding part when we receive any real world problem.
par BJOct 9, 2019
Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras
par ASDec 15, 2019
Queries are not getting resolved in the discussion forum. So, instructors should participate in the discussion forum to resolve such queries.
par VGFeb 1, 2020
This course is a piece of art. I see how carefully and precise course was build and recorded. Thank you for awesome experience!
par KHFeb 25, 2020
This course was great because first it covered theory then goes to code. And the algorithms are taught by the teacher so well.
par LDSep 7, 2019
The teaching is not deep enough to solve the Week 5 assignment, please take note you need to plumb in other Keras courses.
par CHDec 9, 2019
Really enjoyed the class, felt that the level of challenge was appropriate. Thanks for making this class available!
À propos de IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
À propos du Nombre de IBM AI Engineering Certificat Professionnel
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à ce Certificat ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours du Certificat, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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